Minutiae Based Fingerprint Verification using Graph Model
Sonali Sen1, Deyashini Bhattacharya2, Soumili Dey3, Sabarna Nandy4
1Sonali Sen*, Department of Computer Science, St. Xavier’s College, Kolkata, India.
2Deyashini Bhattacharya, Department of Computer Science, St. Xavier’s College, Kolkata, India.
3Soumili Dey, Department of Computer Science, , St. Xavier’s College, Kolkata, India.
4Sabarna Nandy, Department of Computer Science, St. Xavier’s College, Kolkata, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1568-1575 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8165088619/2019©BEIESP | DOI: 10.35940/ijeat.F8165.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fingerprints offer one of the most reliable biometric traits that can be used for uniquely identifying a person. This proposed work demonstrates the use of graph theory in the field of fingerprint identification, in which a fingerprint is casted to a weighted complete graph and a weight matrix of this graph is used to describe the regions in the image and then checked for biometric authentication without considering Henry’s classes. It further implements the concept of graph isomorphism along with edge mapping for matching of fingerprints which portrays the potential of graph-based methods for fingerprint representation, storage, and matching. The proposed algorithm is robust to non-linear distortion, rotation and scaling. The algorithm is tested on a database of Fingerprint Verification Competition (FVC) and has been found to be an efficient and a reliable one as compared to image processing which deals with the entire image for comparison between two fingerprints using pattern recognition.
Keywords: Minutiae, Graph Isomorphism, Sub Graph Isomorphism, Integer Generalized Bresenham Line Draw Algorithm, Fingerprint.